In [1]}, Sam et al. argue that can reach better performance by training the regressor of a multi-column network exploiting only a particular set of the available training patches by leveraging the variation of crowd density within an image. To this end, they proposed a switching cnn-based architecture that selects the optimal regressor suited for a particular input patch. The authors described the network training phase as composed of multiple stages. First, the independent regressors are pre-trained on image patches to minimize the Euclidean distance between the estimated density map and ground truth. Then, the switch classifier, based on VGG-16 architecture [2]}, is trained to select the optimal regressor for accurate counting. Finally, the switch classifier and the cnn-based regressors are co-adapted in the coupled training stage.
More, Kumagai et al. [3]} proposed a Mixture of cnn (MoCNN), consisting of a mixture of expert cnn and a gating cnn that adaptively selects the appropriate cnn among the experts, according to the appearance of the input image. In particular, the gating cnn predicts appropriate probabilities for each of the experts; these probabilities are further used as weighting factors to compute the weighted average of the counts predicted by all the experts. The overview of the architecture is shown in fig:overviewmixturecnn.
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